Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations942
Missing cells471
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory588.8 KiB
Average record size in memory640.1 B

Variable types

Text2
Numeric11
Categorical9
Boolean3

Alerts

brand_name is highly overall correlated with has_ir_blaster and 6 other fieldsHigh correlation
extended_memory_available is highly overall correlated with extended_upto and 7 other fieldsHigh correlation
extended_upto is highly overall correlated with extended_memory_available and 5 other fieldsHigh correlation
fast_charging is highly overall correlated with extended_memory_available and 9 other fieldsHigh correlation
fast_charging_available is highly overall correlated with fast_charging and 5 other fieldsHigh correlation
has_5g is highly overall correlated with extended_memory_available and 8 other fieldsHigh correlation
has_ir_blaster is highly overall correlated with brand_nameHigh correlation
has_nfc is highly overall correlated with brand_name and 5 other fieldsHigh correlation
internal_memory is highly overall correlated with fast_charging and 7 other fieldsHigh correlation
num_cores is highly overall correlated with brand_name and 4 other fieldsHigh correlation
num_rear_cameras is highly overall correlated with fast_charging_availableHigh correlation
os is highly overall correlated with brand_name and 2 other fieldsHigh correlation
price is highly overall correlated with extended_memory_available and 10 other fieldsHigh correlation
primary_camera_front is highly overall correlated with fast_charging and 6 other fieldsHigh correlation
primary_camera_rear is highly overall correlated with fast_charging_available and 2 other fieldsHigh correlation
processor_brand is highly overall correlated with brand_name and 3 other fieldsHigh correlation
processor_speed is highly overall correlated with extended_memory_available and 10 other fieldsHigh correlation
ram_capacity is highly overall correlated with extended_memory_available and 9 other fieldsHigh correlation
rating is highly overall correlated with fast_charging and 9 other fieldsHigh correlation
refresh_rate is highly overall correlated with brand_name and 10 other fieldsHigh correlation
screen_size is highly overall correlated with brand_nameHigh correlation
os is highly imbalanced (80.5%) Imbalance
num_cores is highly imbalanced (72.7%) Imbalance
num_front_cameras is highly imbalanced (88.8%) Imbalance
rating has 85 (9.0%) missing values Missing
processor_speed has 41 (4.4%) missing values Missing
processor_brand has 20 (2.1%) missing values Missing
fast_charging has 195 (20.7%) missing values Missing
extended_upto has 116 (12.3%) missing values Missing
model has unique values Unique
extended_upto has 329 (34.9%) zeros Zeros

Reproduction

Analysis started2025-02-07 14:22:16.878238
Analysis finished2025-02-07 14:22:48.762813
Duration31.88 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

model
Text

Unique 

Distinct942
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.5 KiB
2025-02-07T19:52:49.470596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length50
Median length39
Mean length20.352442
Min length6

Characters and Unicode

Total characters19172
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique942 ?
Unique (%)100.0%

Sample

1st rowOnePlus 11 5G
2nd rowOnePlus Nord CE 2 Lite 5G
3rd rowSamsung Galaxy A14 5G
4th rowMotorola Moto G62 5G
5th rowRealme 10 Pro Plus
ValueCountFrequency (%)
5g 300
 
7.2%
ram 218
 
5.2%
212
 
5.1%
pro 198
 
4.8%
128gb 133
 
3.2%
xiaomi 130
 
3.1%
galaxy 129
 
3.1%
samsung 129
 
3.1%
vivo 106
 
2.5%
redmi 102
 
2.4%
Other values (496) 2507
60.2%
2025-02-07T19:52:50.396868image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3222
 
16.8%
o 1192
 
6.2%
G 986
 
5.1%
a 849
 
4.4%
i 831
 
4.3%
e 765
 
4.0%
1 587
 
3.1%
P 560
 
2.9%
2 508
 
2.6%
m 494
 
2.6%
Other values (56) 9178
47.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3222
 
16.8%
o 1192
 
6.2%
G 986
 
5.1%
a 849
 
4.4%
i 831
 
4.3%
e 765
 
4.0%
1 587
 
3.1%
P 560
 
2.9%
2 508
 
2.6%
m 494
 
2.6%
Other values (56) 9178
47.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3222
 
16.8%
o 1192
 
6.2%
G 986
 
5.1%
a 849
 
4.4%
i 831
 
4.3%
e 765
 
4.0%
1 587
 
3.1%
P 560
 
2.9%
2 508
 
2.6%
m 494
 
2.6%
Other values (56) 9178
47.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3222
 
16.8%
o 1192
 
6.2%
G 986
 
5.1%
a 849
 
4.4%
i 831
 
4.3%
e 765
 
4.0%
1 587
 
3.1%
P 560
 
2.9%
2 508
 
2.6%
m 494
 
2.6%
Other values (56) 9178
47.9%

price
Real number (ℝ)

High correlation 

Distinct357
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28611.932
Minimum3499
Maximum182999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2025-02-07T19:52:50.621524image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum3499
5-th percentile7300.05
Q112999
median19984
Q332999
95-th percentile82950.45
Maximum182999
Range179500
Interquartile range (IQR)20000

Descriptive statistics

Standard deviation26063.178
Coefficient of variation (CV)0.91091989
Kurtosis7.582136
Mean28611.932
Median Absolute Deviation (MAD)8485
Skewness2.4766587
Sum26952440
Variance6.7928924 × 108
MonotonicityNot monotonic
2025-02-07T19:52:50.903670image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14999 21
 
2.2%
11999 17
 
1.8%
19990 17
 
1.8%
16999 16
 
1.7%
19999 16
 
1.7%
17999 15
 
1.6%
15999 15
 
1.6%
13999 15
 
1.6%
8999 14
 
1.5%
9999 14
 
1.5%
Other values (347) 782
83.0%
ValueCountFrequency (%)
3499 1
0.1%
3890 1
0.1%
3990 1
0.1%
3999 1
0.1%
4499 1
0.1%
4649 1
0.1%
4787 1
0.1%
4999 1
0.1%
5249 1
0.1%
5299 1
0.1%
ValueCountFrequency (%)
182999 1
0.1%
179900 1
0.1%
172999 1
0.1%
169900 1
0.1%
149900 1
0.1%
147900 1
0.1%
142990 1
0.1%
139990 1
0.1%
139900 1
0.1%
134999 1
0.1%

rating
Real number (ℝ)

High correlation  Missing 

Distinct30
Distinct (%)3.5%
Missing85
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean78.23804
Minimum60
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2025-02-07T19:52:51.151088image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile63
Q174
median80
Q384
95-th percentile88
Maximum89
Range29
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.3863223
Coefficient of variation (CV)0.094408325
Kurtosis-0.31040465
Mean78.23804
Median Absolute Deviation (MAD)5
Skewness-0.70043336
Sum67050
Variance54.557757
MonotonicityNot monotonic
2025-02-07T19:52:51.412662image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
84 59
 
6.3%
82 57
 
6.1%
83 55
 
5.8%
85 50
 
5.3%
75 49
 
5.2%
80 47
 
5.0%
86 46
 
4.9%
79 45
 
4.8%
77 38
 
4.0%
78 36
 
3.8%
Other values (20) 375
39.8%
(Missing) 85
 
9.0%
ValueCountFrequency (%)
60 11
1.2%
61 15
1.6%
62 10
1.1%
63 10
1.1%
64 10
1.1%
65 14
1.5%
66 15
1.6%
67 13
1.4%
68 13
1.4%
69 18
1.9%
ValueCountFrequency (%)
89 33
3.5%
88 24
2.5%
87 31
3.3%
86 46
4.9%
85 50
5.3%
84 59
6.3%
83 55
5.8%
82 57
6.1%
81 34
3.6%
80 47
5.0%

os
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing8
Missing (%)0.8%
Memory size66.0 KiB
android
890 
ios
 
35
other
 
9

Length

Max length7
Median length7
Mean length6.8308351
Min length3

Characters and Unicode

Total characters6380
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowandroid
2nd rowandroid
3rd rowandroid
4th rowandroid
5th rowandroid

Common Values

ValueCountFrequency (%)
android 890
94.5%
ios 35
 
3.7%
other 9
 
1.0%
(Missing) 8
 
0.8%

Length

2025-02-07T19:52:51.655297image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T19:52:51.870503image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
android 890
95.3%
ios 35
 
3.7%
other 9
 
1.0%

Most occurring characters

ValueCountFrequency (%)
d 1780
27.9%
o 934
14.6%
i 925
14.5%
r 899
14.1%
n 890
13.9%
a 890
13.9%
s 35
 
0.5%
t 9
 
0.1%
h 9
 
0.1%
e 9
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1780
27.9%
o 934
14.6%
i 925
14.5%
r 899
14.1%
n 890
13.9%
a 890
13.9%
s 35
 
0.5%
t 9
 
0.1%
h 9
 
0.1%
e 9
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1780
27.9%
o 934
14.6%
i 925
14.5%
r 899
14.1%
n 890
13.9%
a 890
13.9%
s 35
 
0.5%
t 9
 
0.1%
h 9
 
0.1%
e 9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1780
27.9%
o 934
14.6%
i 925
14.5%
r 899
14.1%
n 890
13.9%
a 890
13.9%
s 35
 
0.5%
t 9
 
0.1%
h 9
 
0.1%
e 9
 
0.1%

brand_name
Categorical

High correlation 

Distinct43
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size64.9 KiB
xiaomi
130 
samsung
129 
vivo
106 
realme
97 
oppo
84 
Other values (38)
396 

Length

Max length9
Median length8
Mean length5.5424628
Min length2

Characters and Unicode

Total characters5221
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.2%

Sample

1st rowoneplus
2nd rowoneplus
3rd rowsamsung
4th rowmotorola
5th rowrealme

Common Values

ValueCountFrequency (%)
xiaomi 130
13.8%
samsung 129
13.7%
vivo 106
11.3%
realme 97
10.3%
oppo 84
8.9%
motorola 52
 
5.5%
oneplus 42
 
4.5%
poco 41
 
4.4%
apple 35
 
3.7%
tecno 33
 
3.5%
Other values (33) 193
20.5%

Length

2025-02-07T19:52:52.090692image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xiaomi 130
13.8%
samsung 129
13.7%
vivo 106
11.3%
realme 97
10.3%
oppo 84
8.9%
motorola 52
 
5.5%
oneplus 42
 
4.5%
poco 41
 
4.4%
apple 35
 
3.7%
tecno 33
 
3.5%
Other values (33) 193
20.5%

Most occurring characters

ValueCountFrequency (%)
o 881
16.9%
i 550
10.5%
a 500
9.6%
m 417
 
8.0%
e 363
 
7.0%
s 325
 
6.2%
p 322
 
6.2%
n 319
 
6.1%
l 277
 
5.3%
v 222
 
4.3%
Other values (16) 1045
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 881
16.9%
i 550
10.5%
a 500
9.6%
m 417
 
8.0%
e 363
 
7.0%
s 325
 
6.2%
p 322
 
6.2%
n 319
 
6.1%
l 277
 
5.3%
v 222
 
4.3%
Other values (16) 1045
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 881
16.9%
i 550
10.5%
a 500
9.6%
m 417
 
8.0%
e 363
 
7.0%
s 325
 
6.2%
p 322
 
6.2%
n 319
 
6.1%
l 277
 
5.3%
v 222
 
4.3%
Other values (16) 1045
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 881
16.9%
i 550
10.5%
a 500
9.6%
m 417
 
8.0%
e 363
 
7.0%
s 325
 
6.2%
p 322
 
6.2%
n 319
 
6.1%
l 277
 
5.3%
v 222
 
4.3%
Other values (16) 1045
20.0%

has_5g
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.3 KiB
True
519 
False
423 
ValueCountFrequency (%)
True 519
55.1%
False 423
44.9%
2025-02-07T19:52:52.372389image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

has_nfc
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.3 KiB
False
581 
True
361 
ValueCountFrequency (%)
False 581
61.7%
True 361
38.3%
2025-02-07T19:52:52.514523image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

has_ir_blaster
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.3 KiB
False
790 
True
152 
ValueCountFrequency (%)
False 790
83.9%
True 152
 
16.1%
2025-02-07T19:52:52.656283image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

num_cores
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing5
Missing (%)0.5%
Memory size62.5 KiB
8.0
872 
4.0
 
36
6.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2811
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8.0
2nd row8.0
3rd row8.0
4th row8.0
5th row8.0

Common Values

ValueCountFrequency (%)
8.0 872
92.6%
4.0 36
 
3.8%
6.0 29
 
3.1%
(Missing) 5
 
0.5%

Length

2025-02-07T19:52:52.829727image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T19:52:52.999973image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
8.0 872
93.1%
4.0 36
 
3.8%
6.0 29
 
3.1%

Most occurring characters

ValueCountFrequency (%)
. 937
33.3%
0 937
33.3%
8 872
31.0%
4 36
 
1.3%
6 29
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2811
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 937
33.3%
0 937
33.3%
8 872
31.0%
4 36
 
1.3%
6 29
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2811
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 937
33.3%
0 937
33.3%
8 872
31.0%
4 36
 
1.3%
6 29
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2811
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 937
33.3%
0 937
33.3%
8 872
31.0%
4 36
 
1.3%
6 29
 
1.0%

processor_speed
Real number (ℝ)

High correlation  Missing 

Distinct35
Distinct (%)3.9%
Missing41
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean2.4075805
Minimum1.2
Maximum3.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2025-02-07T19:52:53.188571image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.8
Q12.05
median2.3
Q32.84
95-th percentile3.2
Maximum3.22
Range2.02
Interquartile range (IQR)0.79

Descriptive statistics

Standard deviation0.45646775
Coefficient of variation (CV)0.18959605
Kurtosis-0.54643226
Mean2.4075805
Median Absolute Deviation (MAD)0.3
Skewness0.24668622
Sum2169.23
Variance0.20836281
MonotonicityNot monotonic
2025-02-07T19:52:53.452950image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 144
15.3%
2.2 134
14.2%
2.4 128
13.6%
3.2 86
9.1%
2.3 86
9.1%
3 50
 
5.3%
2.84 29
 
3.1%
2.05 28
 
3.0%
1.8 23
 
2.4%
2.5 23
 
2.4%
Other values (25) 170
18.0%
(Missing) 41
 
4.4%
ValueCountFrequency (%)
1.2 1
 
0.1%
1.3 10
 
1.1%
1.4 5
 
0.5%
1.5 3
 
0.3%
1.6 20
 
2.1%
1.8 23
 
2.4%
1.82 10
 
1.1%
1.95 1
 
0.1%
1.99 1
 
0.1%
2 144
15.3%
ValueCountFrequency (%)
3.22 16
 
1.7%
3.2 86
9.1%
3.13 2
 
0.2%
3.1 7
 
0.7%
3.05 8
 
0.8%
3 50
5.3%
2.96 2
 
0.2%
2.9 13
 
1.4%
2.86 1
 
0.1%
2.85 19
 
2.0%

processor_brand
Categorical

High correlation  Missing 

Distinct13
Distinct (%)1.4%
Missing20
Missing (%)2.1%
Memory size67.0 KiB
snapdragon
392 
helio
198 
dimensity
176 
exynos
50 
bionic
 
34
Other values (8)
72 

Length

Max length10
Median length9
Mean length8.0433839
Min length3

Characters and Unicode

Total characters7416
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowsnapdragon
2nd rowsnapdragon
3rd rowexynos
4th rowsnapdragon
5th rowdimensity

Common Values

ValueCountFrequency (%)
snapdragon 392
41.6%
helio 198
21.0%
dimensity 176
18.7%
exynos 50
 
5.3%
bionic 34
 
3.6%
unisoc 26
 
2.8%
tiger 24
 
2.5%
google 9
 
1.0%
kirin 5
 
0.5%
spreadtrum 4
 
0.4%
Other values (3) 4
 
0.4%
(Missing) 20
 
2.1%

Length

2025-02-07T19:52:53.787216image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
snapdragon 392
42.5%
helio 198
21.5%
dimensity 176
19.1%
exynos 50
 
5.4%
bionic 34
 
3.7%
unisoc 26
 
2.8%
tiger 24
 
2.6%
google 9
 
1.0%
kirin 5
 
0.5%
spreadtrum 4
 
0.4%
Other values (3) 4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
n 1076
14.5%
a 790
10.7%
o 719
9.7%
i 679
9.2%
s 651
8.8%
d 572
7.7%
e 461
 
6.2%
g 434
 
5.9%
r 429
 
5.8%
p 396
 
5.3%
Other values (17) 1209
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1076
14.5%
a 790
10.7%
o 719
9.7%
i 679
9.2%
s 651
8.8%
d 572
7.7%
e 461
 
6.2%
g 434
 
5.9%
r 429
 
5.8%
p 396
 
5.3%
Other values (17) 1209
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1076
14.5%
a 790
10.7%
o 719
9.7%
i 679
9.2%
s 651
8.8%
d 572
7.7%
e 461
 
6.2%
g 434
 
5.9%
r 429
 
5.8%
p 396
 
5.3%
Other values (17) 1209
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1076
14.5%
a 790
10.7%
o 719
9.7%
i 679
9.2%
s 651
8.8%
d 572
7.7%
e 461
 
6.2%
g 434
 
5.9%
r 429
 
5.8%
p 396
 
5.3%
Other values (17) 1209
16.3%

ram_capacity
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4808917
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2025-02-07T19:52:53.967653image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q38
95-th percentile12
Maximum18
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6880212
Coefficient of variation (CV)0.41476101
Kurtosis1.3022908
Mean6.4808917
Median Absolute Deviation (MAD)2
Skewness0.78438868
Sum6105
Variance7.2254581
MonotonicityNot monotonic
2025-02-07T19:52:54.164712image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8 329
34.9%
6 231
24.5%
4 209
22.2%
12 70
 
7.4%
3 54
 
5.7%
2 31
 
3.3%
16 9
 
1.0%
1 7
 
0.7%
18 2
 
0.2%
ValueCountFrequency (%)
1 7
 
0.7%
2 31
 
3.3%
3 54
 
5.7%
4 209
22.2%
6 231
24.5%
8 329
34.9%
12 70
 
7.4%
16 9
 
1.0%
18 2
 
0.2%
ValueCountFrequency (%)
18 2
 
0.2%
16 9
 
1.0%
12 70
 
7.4%
8 329
34.9%
6 231
24.5%
4 209
22.2%
3 54
 
5.7%
2 31
 
3.3%
1 7
 
0.7%

internal_memory
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.24416
Minimum8
Maximum1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2025-02-07T19:52:54.499258image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile32
Q164
median128
Q3128
95-th percentile256
Maximum1024
Range1016
Interquartile range (IQR)64

Descriptive statistics

Standard deviation98.101196
Coefficient of variation (CV)0.72536363
Kurtosis29.582399
Mean135.24416
Median Absolute Deviation (MAD)0
Skewness4.1027798
Sum127400
Variance9623.8447
MonotonicityNot monotonic
2025-02-07T19:52:54.696915image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
128 517
54.9%
64 188
 
20.0%
256 139
 
14.8%
32 67
 
7.1%
512 14
 
1.5%
16 12
 
1.3%
1024 4
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
16 12
 
1.3%
32 67
 
7.1%
64 188
 
20.0%
128 517
54.9%
256 139
 
14.8%
512 14
 
1.5%
1024 4
 
0.4%
ValueCountFrequency (%)
1024 4
 
0.4%
512 14
 
1.5%
256 139
 
14.8%
128 517
54.9%
64 188
 
20.0%
32 67
 
7.1%
16 12
 
1.3%
8 1
 
0.1%

battery_capacity
Real number (ℝ)

Distinct83
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4784.1752
Minimum1821
Maximum7000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2025-02-07T19:52:54.916930image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1821
5-th percentile3500
Q14500
median5000
Q35000
95-th percentile6000
Maximum7000
Range5179
Interquartile range (IQR)500

Descriptive statistics

Standard deviation636.73504
Coefficient of variation (CV)0.13309192
Kurtosis3.7646755
Mean4784.1752
Median Absolute Deviation (MAD)0
Skewness-0.83157311
Sum4506693
Variance405431.51
MonotonicityNot monotonic
2025-02-07T19:52:55.190626image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 485
51.5%
4500 93
 
9.9%
6000 60
 
6.4%
4000 40
 
4.2%
4700 26
 
2.8%
4300 24
 
2.5%
4800 17
 
1.8%
4200 13
 
1.4%
5020 10
 
1.1%
4230 7
 
0.7%
Other values (73) 167
 
17.7%
ValueCountFrequency (%)
1821 1
0.1%
1900 1
0.1%
2000 1
0.1%
2050 1
0.1%
2150 1
0.1%
2230 1
0.1%
2400 1
0.1%
2438 1
0.1%
2500 1
0.1%
2730 1
0.1%
ValueCountFrequency (%)
7000 6
 
0.6%
6000 60
6.4%
5600 1
 
0.1%
5500 4
 
0.4%
5200 3
 
0.3%
5180 1
 
0.1%
5160 5
 
0.5%
5100 4
 
0.4%
5080 6
 
0.6%
5065 1
 
0.1%

fast_charging
Real number (ℝ)

High correlation  Missing 

Distinct31
Distinct (%)4.1%
Missing195
Missing (%)20.7%
Infinite0
Infinite (%)0.0%
Mean45.925033
Minimum10
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2025-02-07T19:52:55.426241image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q118
median33
Q366
95-th percentile120
Maximum240
Range230
Interquartile range (IQR)48

Descriptive statistics

Standard deviation34.61008
Coefficient of variation (CV)0.75362123
Kurtosis3.3842813
Mean45.925033
Median Absolute Deviation (MAD)15
Skewness1.6595102
Sum34306
Variance1197.8576
MonotonicityNot monotonic
2025-02-07T19:52:56.024447image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
33 149
15.8%
18 128
13.6%
67 61
 
6.5%
25 50
 
5.3%
120 46
 
4.9%
15 43
 
4.6%
80 40
 
4.2%
10 33
 
3.5%
30 32
 
3.4%
66 31
 
3.3%
Other values (21) 134
14.2%
(Missing) 195
20.7%
ValueCountFrequency (%)
10 33
 
3.5%
15 43
 
4.6%
18 128
13.6%
19 1
 
0.1%
20 10
 
1.1%
21 2
 
0.2%
22 5
 
0.5%
25 50
 
5.3%
30 32
 
3.4%
33 149
15.8%
ValueCountFrequency (%)
240 1
 
0.1%
210 2
 
0.2%
200 1
 
0.1%
180 1
 
0.1%
165 1
 
0.1%
150 7
 
0.7%
135 1
 
0.1%
125 6
 
0.6%
120 46
4.9%
100 7
 
0.7%

screen_size
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5252017
Minimum3.54
Maximum6.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2025-02-07T19:52:56.305716image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum3.54
5-th percentile6.1
Q16.5
median6.58
Q36.67
95-th percentile6.8
Maximum6.95
Range3.41
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.29214388
Coefficient of variation (CV)0.044771624
Kurtosis28.627834
Mean6.5252017
Median Absolute Deviation (MAD)0.09
Skewness-4.3695414
Sum6146.74
Variance0.085348046
MonotonicityNot monotonic
2025-02-07T19:52:56.586281image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 119
 
12.6%
6.67 97
 
10.3%
6.7 93
 
9.9%
6.6 91
 
9.7%
6.4 43
 
4.6%
6.43 43
 
4.6%
6.58 39
 
4.1%
6.8 35
 
3.7%
6.78 31
 
3.3%
6.44 31
 
3.3%
Other values (57) 320
34.0%
ValueCountFrequency (%)
3.54 1
 
0.1%
4 1
 
0.1%
4.5 1
 
0.1%
4.7 3
0.3%
5 5
0.5%
5.2 1
 
0.1%
5.3 1
 
0.1%
5.4 1
 
0.1%
5.42 1
 
0.1%
5.45 2
 
0.2%
ValueCountFrequency (%)
6.95 3
 
0.3%
6.92 1
 
0.1%
6.91 1
 
0.1%
6.9 5
 
0.5%
6.83 2
 
0.2%
6.82 7
 
0.7%
6.81 2
 
0.2%
6.8 35
3.7%
6.78 31
3.3%
6.76 3
 
0.3%
Distinct71
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size121.6 KiB
2025-02-07T19:52:56.925852image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.742038
Min length10

Characters and Unicode

Total characters11061
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)2.5%

Sample

1st row1440 x 3216 
2nd row1080 x 2412 
3rd row1080 x 2408 
4th row1080 x 2400 
5th row1080 x 2412 
ValueCountFrequency (%)
x 942
33.3%
1080 601
21.3%
2400 350
 
12.4%
720 225
 
8.0%
1600 160
 
5.7%
2408 67
 
2.4%
1440 62
 
2.2%
2412 58
 
2.1%
2460 42
 
1.5%
2340 39
 
1.4%
Other values (67) 280
 
9.9%
2025-02-07T19:52:57.477256image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2856
25.8%
2826
25.5%
2 1074
 
9.7%
1 1061
 
9.6%
x 942
 
8.5%
4 770
 
7.0%
8 746
 
6.7%
6 294
 
2.7%
7 280
 
2.5%
3 129
 
1.2%
Other values (2) 83
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11061
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2856
25.8%
2826
25.5%
2 1074
 
9.7%
1 1061
 
9.6%
x 942
 
8.5%
4 770
 
7.0%
8 746
 
6.7%
6 294
 
2.7%
7 280
 
2.5%
3 129
 
1.2%
Other values (2) 83
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11061
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2856
25.8%
2826
25.5%
2 1074
 
9.7%
1 1061
 
9.6%
x 942
 
8.5%
4 770
 
7.0%
8 746
 
6.7%
6 294
 
2.7%
7 280
 
2.5%
3 129
 
1.2%
Other values (2) 83
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11061
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2856
25.8%
2826
25.5%
2 1074
 
9.7%
1 1061
 
9.6%
x 942
 
8.5%
4 770
 
7.0%
8 746
 
6.7%
6 294
 
2.7%
7 280
 
2.5%
3 129
 
1.2%
Other values (2) 83
 
0.8%

refresh_rate
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.442675
Minimum60
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2025-02-07T19:52:57.651993image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile60
Q160
median90
Q3120
95-th percentile144
Maximum240
Range180
Interquartile range (IQR)60

Descriptive statistics

Standard deviation28.951357
Coefficient of variation (CV)0.31318173
Kurtosis-0.61470919
Mean92.442675
Median Absolute Deviation (MAD)30
Skewness0.30511435
Sum87081
Variance838.18109
MonotonicityNot monotonic
2025-02-07T19:52:57.858911image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
60 348
36.9%
120 327
34.7%
90 218
23.1%
144 39
 
4.1%
165 9
 
1.0%
240 1
 
0.1%
ValueCountFrequency (%)
60 348
36.9%
90 218
23.1%
120 327
34.7%
144 39
 
4.1%
165 9
 
1.0%
240 1
 
0.1%
ValueCountFrequency (%)
240 1
 
0.1%
165 9
 
1.0%
144 39
 
4.1%
120 327
34.7%
90 218
23.1%
60 348
36.9%

num_rear_cameras
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
3
529 
2
201 
4
150 
1
62 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters942
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 529
56.2%
2 201
 
21.3%
4 150
 
15.9%
1 62
 
6.6%

Length

2025-02-07T19:52:58.064268image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T19:52:58.252600image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
3 529
56.2%
2 201
 
21.3%
4 150
 
15.9%
1 62
 
6.6%

Most occurring characters

ValueCountFrequency (%)
3 529
56.2%
2 201
 
21.3%
4 150
 
15.9%
1 62
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 529
56.2%
2 201
 
21.3%
4 150
 
15.9%
1 62
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 529
56.2%
2 201
 
21.3%
4 150
 
15.9%
1 62
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 529
56.2%
2 201
 
21.3%
4 150
 
15.9%
1 62
 
6.6%

num_front_cameras
Categorical

Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
1
918 
2
 
23
Missing
 
1

Length

Max length7
Median length1
Mean length1.0063694
Min length1

Characters and Unicode

Total characters948
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 918
97.5%
2 23
 
2.4%
Missing 1
 
0.1%

Length

2025-02-07T19:52:58.465290image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T19:52:58.687014image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 918
97.5%
2 23
 
2.4%
missing 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 918
96.8%
2 23
 
2.4%
i 2
 
0.2%
s 2
 
0.2%
M 1
 
0.1%
n 1
 
0.1%
g 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 948
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 918
96.8%
2 23
 
2.4%
i 2
 
0.2%
s 2
 
0.2%
M 1
 
0.1%
n 1
 
0.1%
g 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 948
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 918
96.8%
2 23
 
2.4%
i 2
 
0.2%
s 2
 
0.2%
M 1
 
0.1%
n 1
 
0.1%
g 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 948
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 918
96.8%
2 23
 
2.4%
i 2
 
0.2%
s 2
 
0.2%
M 1
 
0.1%
n 1
 
0.1%
g 1
 
0.1%

primary_camera_rear
Categorical

High correlation 

Distinct17
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size61.7 KiB
50
319 
64
176 
13
115 
48
112 
108
77 
Other values (12)
143 

Length

Max length4
Median length2
Mean length2.0679406
Min length1

Characters and Unicode

Total characters1948
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row50
2nd row64
3rd row50
4th row50
5th row108

Common Values

ValueCountFrequency (%)
50 319
33.9%
64 176
18.7%
13 115
 
12.2%
48 112
 
11.9%
108 77
 
8.2%
12 46
 
4.9%
8 39
 
4.1%
200 18
 
1.9%
16 16
 
1.7%
5 6
 
0.6%
Other values (7) 18
 
1.9%

Length

2025-02-07T19:52:58.935951image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
50 319
33.9%
64 176
18.7%
13 115
 
12.2%
48 112
 
11.9%
108 77
 
8.2%
12 46
 
4.9%
8 39
 
4.1%
200 18
 
1.9%
16 16
 
1.7%
5 6
 
0.6%
Other values (7) 18
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 438
22.5%
5 331
17.0%
4 294
15.1%
1 258
13.2%
8 228
11.7%
6 192
9.9%
3 118
 
6.1%
2 80
 
4.1%
. 8
 
0.4%
7 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1948
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 438
22.5%
5 331
17.0%
4 294
15.1%
1 258
13.2%
8 228
11.7%
6 192
9.9%
3 118
 
6.1%
2 80
 
4.1%
. 8
 
0.4%
7 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1948
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 438
22.5%
5 331
17.0%
4 294
15.1%
1 258
13.2%
8 228
11.7%
6 192
9.9%
3 118
 
6.1%
2 80
 
4.1%
. 8
 
0.4%
7 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1948
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 438
22.5%
5 331
17.0%
4 294
15.1%
1 258
13.2%
8 228
11.7%
6 192
9.9%
3 118
 
6.1%
2 80
 
4.1%
. 8
 
0.4%
7 1
 
0.1%

primary_camera_front
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)2.3%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean16.583209
Minimum0.3
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2025-02-07T19:52:59.149473image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile5
Q18
median16
Q316
95-th percentile32
Maximum60
Range59.7
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.952164
Coefficient of variation (CV)0.66043691
Kurtosis2.2485214
Mean16.583209
Median Absolute Deviation (MAD)8
Skewness1.4475058
Sum15604.8
Variance119.94989
MonotonicityNot monotonic
2025-02-07T19:52:59.384724image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
16 301
32.0%
8 177
18.8%
32 148
15.7%
5 119
 
12.6%
12 41
 
4.4%
13 40
 
4.2%
20 34
 
3.6%
10 21
 
2.2%
50 12
 
1.3%
60 10
 
1.1%
Other values (12) 38
 
4.0%
ValueCountFrequency (%)
0.3 1
 
0.1%
2 5
 
0.5%
5 119
12.6%
7 4
 
0.4%
8 177
18.8%
10 21
 
2.2%
10.1 1
 
0.1%
10.8 2
 
0.2%
11.1 2
 
0.2%
12 41
 
4.4%
ValueCountFrequency (%)
60 10
 
1.1%
50 12
 
1.3%
48 2
 
0.2%
44 8
 
0.8%
40 6
 
0.6%
32 148
15.7%
25 3
 
0.3%
24 3
 
0.3%
20 34
 
3.6%
16 301
32.0%

fast_charging_available
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
1
814 
0
128 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters942
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 814
86.4%
0 128
 
13.6%

Length

2025-02-07T19:52:59.632978image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T19:52:59.854121image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 814
86.4%
0 128
 
13.6%

Most occurring characters

ValueCountFrequency (%)
1 814
86.4%
0 128
 
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 814
86.4%
0 128
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 814
86.4%
0 128
 
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 814
86.4%
0 128
 
13.6%

extended_memory_available
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
1
613 
0
329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters942
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 613
65.1%
0 329
34.9%

Length

2025-02-07T19:53:00.055708image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T19:53:00.240821image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 613
65.1%
0 329
34.9%

Most occurring characters

ValueCountFrequency (%)
1 613
65.1%
0 329
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 613
65.1%
0 329
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 613
65.1%
0 329
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 613
65.1%
0 329
34.9%

extended_upto
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct8
Distinct (%)1.0%
Missing116
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean442.46005
Minimum0
Maximum2048
Zeros329
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2025-02-07T19:53:00.419959image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median256
Q31024
95-th percentile1024
Maximum2048
Range2048
Interquartile range (IQR)1024

Descriptive statistics

Standard deviation456.64693
Coefficient of variation (CV)1.0320636
Kurtosis-0.8693752
Mean442.46005
Median Absolute Deviation (MAD)256
Skewness0.54451911
Sum365472
Variance208526.42
MonotonicityNot monotonic
2025-02-07T19:53:00.619614image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 329
34.9%
1024 263
27.9%
512 116
 
12.3%
256 98
 
10.4%
128 9
 
1.0%
2048 5
 
0.5%
32 3
 
0.3%
64 3
 
0.3%
(Missing) 116
 
12.3%
ValueCountFrequency (%)
0 329
34.9%
32 3
 
0.3%
64 3
 
0.3%
128 9
 
1.0%
256 98
 
10.4%
512 116
 
12.3%
1024 263
27.9%
2048 5
 
0.5%
ValueCountFrequency (%)
2048 5
 
0.5%
1024 263
27.9%
512 116
 
12.3%
256 98
 
10.4%
128 9
 
1.0%
64 3
 
0.3%
32 3
 
0.3%
0 329
34.9%

Interactions

2025-02-07T19:52:45.017937image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:20.260032image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:22.733546image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:25.355989image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:27.740143image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:30.379248image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:32.969383image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:35.596462image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:37.696769image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:40.201984image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:42.440708image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:45.234897image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:20.502030image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:22.920219image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:25.590293image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:27.974245image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:30.633851image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:33.179985image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:35.785031image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:37.958699image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:40.418998image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:42.647063image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:45.412677image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:20.692318image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:23.119981image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:25.802876image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:28.207075image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:30.847151image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:33.396287image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:35.972176image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:38.277874image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:40.623254image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:42.849082image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:45.636520image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:20.930351image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:23.338418image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:26.035402image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:28.504894image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:31.118974image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:33.627011image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:36.183745image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:38.522159image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:40.823172image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:43.092164image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:45.839095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:21.167831image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:23.547614image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:26.261377image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:28.736538image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:31.395818image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:34.221179image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:36.384233image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:38.763818image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:41.051726image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:43.284221image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:46.027466image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:21.369730image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:23.739314image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:26.475424image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:28.947012image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:31.629468image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:34.431226image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:36.552942image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:38.970422image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:41.255685image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:43.780528image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:46.210683image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:21.693241image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:23.938181image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:26.690453image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:29.180784image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:31.860571image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:34.619469image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:36.740623image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:39.192484image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:41.444051image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:43.984482image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:46.386789image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:21.892013image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:24.112655image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:26.895788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:29.490676image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:32.066369image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:34.807818image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:36.929007image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:39.388927image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:41.647748image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:44.172664image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:46.601607image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:22.113884image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:24.309200image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:27.118715image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:29.728941image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:32.295177image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:35.020724image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:37.106359image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:39.603989image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:41.855940image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:44.376630image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:46.783020image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:22.323637image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:24.970577image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:27.326251image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:29.952069image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:32.539509image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:35.225567image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:37.301397image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:39.805586image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:42.044313image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:44.596639image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:46.986984image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:22.528186image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:25.178987image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:27.540265image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:30.177236image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:32.764507image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:35.423766image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:37.499029image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:40.016504image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:42.247777image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-07T19:52:44.843577image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2025-02-07T19:53:00.884395image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
battery_capacitybrand_nameextended_memory_availableextended_uptofast_chargingfast_charging_availablehas_5ghas_ir_blasterhas_nfcinternal_memorynum_coresnum_front_camerasnum_rear_camerasospriceprimary_camera_frontprimary_camera_rearprocessor_brandprocessor_speedram_capacityratingrefresh_ratescreen_size
battery_capacity1.0000.3620.4000.359-0.2540.2760.3360.1960.365-0.1100.4320.1750.2680.399-0.313-0.1290.2680.261-0.176-0.132-0.2570.0620.333
brand_name0.3621.0000.4760.4430.2330.4100.3510.8060.5280.2990.7950.1560.3580.8390.2670.2440.4200.5110.3860.3550.1870.5810.548
extended_memory_available0.4000.4761.0000.9610.6280.2130.5020.0310.5550.4730.2450.1000.2500.2690.6680.3410.3300.4810.7250.5100.4730.5150.414
extended_upto0.3590.4430.9611.000-0.5360.2770.5760.2960.577-0.3420.1770.0620.1530.176-0.513-0.2770.2230.301-0.515-0.387-0.341-0.339-0.169
fast_charging-0.2540.2330.628-0.5361.0001.0000.4600.2570.3880.5610.0000.1210.1700.0000.6600.5960.1640.1240.6450.6550.6540.6180.398
fast_charging_available0.2760.4100.2130.2771.0001.0000.3980.1580.2820.4820.3710.0920.5240.0000.3050.5120.6970.4860.5000.5350.6340.4380.369
has_5g0.3360.3510.5020.5760.4600.3981.0000.1040.4800.5960.2350.0140.3680.0880.6010.4300.4710.7250.6720.5810.6230.6450.337
has_ir_blaster0.1960.8060.0310.2960.2570.1580.1041.0000.0230.0680.1110.0000.1830.0750.0000.2890.2860.2160.2070.1560.1850.2020.194
has_nfc0.3650.5280.5550.5770.3880.2820.4800.0231.0000.4840.2680.0000.2450.2400.6720.4060.4210.4980.6350.4690.5490.4610.385
internal_memory-0.1100.2990.473-0.3420.5610.4820.5960.0680.4841.0000.3580.0540.2550.2610.7630.5420.3290.3250.6070.7970.7340.5580.354
num_cores0.4320.7950.2450.1770.0000.3710.2350.1110.2680.3581.0000.1100.2710.6830.4530.3150.6150.7700.5400.3930.3330.1900.426
num_front_cameras0.1750.1560.1000.0620.1210.0920.0140.0000.0000.0540.1101.0000.1130.0000.0720.3850.4930.3600.1680.1310.1230.0050.281
num_rear_cameras0.2680.3580.2500.1530.1700.5240.3680.1830.2450.2550.2710.1131.0000.0950.1940.4040.4460.3000.3300.3310.4050.2690.325
os0.3990.8390.2690.1760.0000.0000.0880.0750.2400.2610.6830.0000.0951.0000.4650.2170.4150.7350.2350.1100.0470.0590.319
price-0.3130.2670.668-0.5130.6600.3050.6010.0000.6720.7630.4530.0720.1940.4651.0000.5870.2530.2820.7880.7710.8010.6040.300
primary_camera_front-0.1290.2440.341-0.2770.5960.5120.4300.2890.4060.5420.3150.3850.4040.2170.5871.0000.3360.2280.4860.6470.6830.5040.245
primary_camera_rear0.2680.4200.3300.2230.1640.6970.4710.2860.4210.3290.6150.4930.4460.4150.2530.3361.0000.3070.3400.3630.3680.5290.368
processor_brand0.2610.5110.4810.3010.1240.4860.7250.2160.4980.3250.7700.3600.3000.7350.2820.2280.3071.0000.4380.2770.2400.2470.317
processor_speed-0.1760.3860.725-0.5150.6450.5000.6720.2070.6350.6070.5400.1680.3300.2350.7880.4860.3400.4381.0000.6280.6820.5770.360
ram_capacity-0.1320.3550.510-0.3870.6550.5350.5810.1560.4690.7970.3930.1310.3310.1100.7710.6470.3630.2770.6281.0000.8370.5900.371
rating-0.2570.1870.473-0.3410.6540.6340.6230.1850.5490.7340.3330.1230.4050.0470.8010.6830.3680.2400.6820.8371.0000.6320.309
refresh_rate0.0620.5810.515-0.3390.6180.4380.6450.2020.4610.5580.1900.0050.2690.0590.6040.5040.5290.2470.5770.5900.6321.0000.471
screen_size0.3330.5480.414-0.1690.3980.3690.3370.1940.3850.3540.4260.2810.3250.3190.3000.2450.3680.3170.3600.3710.3090.4711.000

Missing values

2025-02-07T19:52:47.296065image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-07T19:52:48.038136image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-07T19:52:48.574534image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

modelpriceratingosbrand_namehas_5ghas_nfchas_ir_blasternum_coresprocessor_speedprocessor_brandram_capacityinternal_memorybattery_capacityfast_chargingscreen_sizeresolutionrefresh_ratenum_rear_camerasnum_front_camerasprimary_camera_rearprimary_camera_frontfast_charging_availableextended_memory_availableextended_upto
0OnePlus 11 5G5499989.0androidoneplusTrueTrueFalse8.03.20snapdragon12.0256.05000.0100.06.701440 x 3216120315016.0100.0
1OnePlus Nord CE 2 Lite 5G1998981.0androidoneplusTrueFalseFalse8.02.20snapdragon6.0128.05000.033.06.591080 x 2412120316416.0111024.0
2Samsung Galaxy A14 5G1649975.0androidsamsungTrueFalseFalse8.02.40exynos4.064.05000.015.06.601080 x 240890315013.0111024.0
3Motorola Moto G62 5G1499981.0androidmotorolaTrueFalseFalse8.02.20snapdragon6.0128.05000.0NaN6.551080 x 2400120315016.0111024.0
4Realme 10 Pro Plus2499982.0androidrealmeTrueFalseFalse8.02.60dimensity6.0128.05000.067.06.701080 x 24121203110816.0100.0
5Samsung Galaxy F23 5G (6GB RAM + 128GB)1699980.0androidsamsungTrueTrueFalse8.02.20snapdragon6.0128.05000.025.06.601080 x 240812031508.0111024.0
6Apple iPhone 146599981.0iosappleTrueTrueFalse6.03.22bionic6.0128.03279.0NaN6.101170 x 253260211212.0100.0
7Xiaomi Redmi Note 12 Pro Plus2999986.0androidxiaomiTrueFalseTrue8.02.60dimensity8.0256.04980.0120.06.671080 x 24001203120016.0100.0
8Nothing Phone 12674985.0androidnothingTrueTrueFalse8.02.50snapdragon8.0128.04500.033.06.551080 x 2400120215016.0100.0
9OnePlus Nord 2T 5G2899984.0androidoneplusTrueTrueFalse8.03.00dimensity8.0128.04500.080.06.431080 x 240090315032.0100.0
modelpriceratingosbrand_namehas_5ghas_nfchas_ir_blasternum_coresprocessor_speedprocessor_brandram_capacityinternal_memorybattery_capacityfast_chargingscreen_sizeresolutionrefresh_ratenum_rear_camerasnum_front_camerasprimary_camera_rearprimary_camera_frontfast_charging_availableextended_memory_availableextended_upto
969Xiaomi Civi 33299086.0androidxiaomiTrueTrueTrue8.03.10dimensity8.0256.05000.080.06.701080 x 2400120326432.0100.0
970Realme Narzo 50i Prime (4GB RAM + 64GB)872064.0androidrealmeFalseFalseFalse8.01.82tiger4.064.05000.010.06.50720 x 1600601185.0111024.0
971Oppo Find X66999089.0androidoppoTrueTrueFalse8.03.20snapdragon8.0256.04700.0120.06.731080 x 2400120315032.0100.0
972itel A23s4787NaNandroiditelFalseFalseFalse4.01.40spreadtrum2.032.03020.0NaN5.00854 x 480601Missing2NaN000.0
973Google Pixel 8 Pro7099080.0androidgoogleTrueTrueFalse8.0NaNgoogle12.0256.05000.067.06.731440 x 3120120315012.0100.0
975Motorola Moto Edge S30 Pro3499083.0androidmotorolaTrueFalseFalse8.03.00snapdragon8.0128.05000.068.06.671080 x 2460120316416.0100.0
976Honor X8 5G1499075.0androidhonorTrueFalseFalse8.02.20snapdragon6.0128.05000.022.06.50720 x 16006031488.0111024.0
977POCO X4 GT 5G (8GB RAM + 256GB)2899085.0androidpocoTrueTrueTrue8.02.85dimensity8.0256.05080.067.06.601080 x 2460144316416.0100.0
978Motorola Moto G91 5G1999080.0androidmotorolaTrueTrueFalse8.02.20snapdragon6.0128.05000.0NaN6.801080 x 2400603110832.0111024.0
979Samsung Galaxy M52s 5G2499074.0androidsamsungTrueFalseFalse8.0NaNNaN8.0128.05000.0NaN6.501080 x 240060316432.0111024.0